On their worst day

Posted on 11/16/2010 by


Short but cool post today (Pretty graphs are included)

So Andres dropped a big hint. Thanks to him I now have game split data (Read the Basics if you’re new). This means I can can answer a recurring question I’ve had about WP48. We know the average but what about the variability?

For the purposes of the is discussion I’m going to look at the average WP48 (wins produced per 48) based on game splits for the season so far and work out the variability in ADJP48 (raw productivity). I’m then going to rank all players by their average WP48 – one standard deviation.

The normal distrubution, dig it fools! (see pretty)

In essence that means I can say that that player will be better than that number 84.25% of the time based on the season sample so far.

What does this look like ?

Only 31 players would be expected to deliver above average performances (>.100 WP48) on this list. Landry Fields at .197 is a huge surprise. Chris Paul turns any game he plays this season into a coin flip for his team just for showing up. See here:


Of to do some dishes then bed. Tomorrow rankings and the Smackdown update.

Discuss 🙂

The Picks

The Method for Evaluation

To evaluate how the predictions are doing, I take everyone’s raw wins predictions for each team and combine it with the equation for home team winning I came up with for a single game (see here for detail). To put it simply:

Probability of Home team winning a game (Win %)

= (Projected Wins Home Team-Projected Wins Road Team)/82 +.606

=Win %: (Proj. Home Team Win% – Proj. Road Team Win%) +HCA(.606)

Then I worked it out for every game so far as follows:

  • If W% is greater than 60% call it a strong Win (W) for the home team
  • If W% is greater than 50% but less than 60% call it a weak Win (WW) for the home team
  • If W% is greater than 40% but less than 50% call it a weak Loss (WL) for the home team
  • If W% is less than 40% call it a strong Loss (L) for the home team
  • I then look at everyone’s hit rate for strong predictions and for all predictions.
  • I rank each model/analyst for both
  • I assign points based on ranking (double points for strong predictions since I value those more)
Guy’s question goes to what hit rate means. It’s simply:
Hit Ratio (All): % of all games picked correctly by Win % (so for all 52 games this year)
Hit Ratio (Strong): % of all games were the win % is >60% or less than 40% picked correctly (on average about 33 games per model)
Based on that the results thru yesterday (46 games) looked like:

But I’m intrigued by Guy’s Request. If I:

  • Assign a point per game called correctly
  • Assign an additional point for a strong call
  • Penalize 1 point for a miss.
and redo the tables (updated thru 11/02/10), it looks like:
Take a bow reservoirgod. The WOW analysts are still leading the pack. The Sports Guy is moving up past the other models and Bobo is very angry.
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